Yep. I don't know if that's what is actually being used here, but that is pretty much how they did it with the same system: "We applied the feature learning me

Yep. I don't know if that's what is actually being used here, but that is pretty much how they did it with the same system: "We applied the feature learning method to the task of recognizing objects in the ImageNet dataset (Deng et al., 2009). After unsupervised training on YouTube and ImageNet images, we added one-versus-all logistic classifiers on top of the highest layer. We first trained the logistic classifiers and then fine-tuned the network. Regularization was not employed in the logistic classifiers. The entire training was carried out on 2,000 machines for one week."[1] Basically you learn features in unlabeled data, then identify the features your trained net is recognizing with labeled data. When you run over g+ images, you then only tag with features you're sure of past some threshold of certainty. [1] http://research.google.com/pubs/pub38115.html http://research.google.com/pubs/pub38115.html